Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Ryan is a Senior Machine Learning Engineer developing standards for deploying ML models on geospatial imagery and building Wherobots AI Raster Inference.
Detecting Objects From Text Prompts with RasterFlow and Segment Anything 3
Exploring the capabilities of Segment Anything 3 on high-resolution Earth observation data.
Take-aways from the 2026 Geospatial Embeddings Workshop at Clark University
Some brief take-aways from a workshop to set standards for storing and sharing geospatial embeddings.
Enabling Meta’s SAM 2 model for Geospatial AI on satellite imagery
Easily perform object detection and segmentation on terabyte scale satellite and aerial imagery, on-demand, prompted by text with Raster Inference.
WherobotsAI Raster Inference is GA with Support for Bring Your Own Model
We are excited to announce that WherobotsAI Raster Inference is now generally available! Raster Inference is a serverless, planetary-scale computer vision solution that extracts meaningful insights from aerial imagery (raster data) sources, such as satellites or drones, and puts these insights at the fingertips of data scientists and developers.
Unlock Satellite Imagery Insights with WherobotsAI Raster Inference
Raster Inference makes satellite and drone imagery analytically and economically accessible to common developers
Introducing WherobotsAI for planetary inference, and capabilities that modernize spatial intelligence at scale
UPDATE: Raster inference is included in Wherobots RasterFlow. See Wherobots Get Started with RasterFlow – Wherobots for the most up-to-date workflows. We are excited to announce a preview of WherobotsAI, our new suite of AI and ML powered capabilities that unlock spatial intelligence in satellite imagery and GPS location data. Additionally, we are bringing the […]
